Instructions to use MultiverseComputingCAI/Comet_60B_A12B_BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultiverseComputingCAI/Comet_60B_A12B_BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="MultiverseComputingCAI/Comet_60B_A12B_BF16", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForMultimodalLM
model = AutoModelForMultimodalLM.from_pretrained("MultiverseComputingCAI/Comet_60B_A12B_BF16", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MultiverseComputingCAI/Comet_60B_A12B_BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MultiverseComputingCAI/Comet_60B_A12B_BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultiverseComputingCAI/Comet_60B_A12B_BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/MultiverseComputingCAI/Comet_60B_A12B_BF16

SGLang

How to use MultiverseComputingCAI/Comet_60B_A12B_BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MultiverseComputingCAI/Comet_60B_A12B_BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultiverseComputingCAI/Comet_60B_A12B_BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MultiverseComputingCAI/Comet_60B_A12B_BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultiverseComputingCAI/Comet_60B_A12B_BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use MultiverseComputingCAI/Comet_60B_A12B_BF16 with Docker Model Runner:
```
docker model run hf.co/MultiverseComputingCAI/Comet_60B_A12B_BF16
```

Comet_60B_A12B_BF16

File size: 693 Bytes

ec61017

{
  "_downsample_factor": 2,
  "auto_map": {
    "AutoImageProcessor": "image_processing_nemotron_h_omni.NemotronH_Omni_Reasoning_V3ImageProcessor",
    "AutoProcessor": "processing_nemotron_h_omni.NemotronH_Omni_Reasoning_V3Processor"
  },
  "downsample_ratio": 0.5,
  "image_processor_type": "NemotronH_Omni_Reasoning_V3ImageProcessor",
  "max_model_len": 16384,
  "max_num_patches": 13312,
  "min_num_patches": 1024,
  "norm_mean": [
    0.48145466,
    0.4578275,
    0.40821073
  ],
  "norm_std": [
    0.26862954,
    0.26130258,
    0.27577711
  ],
  "patch_size": 16,
  "rescale_factor": 0.00392156862745098,
  "video_maintain_aspect_ratio": true,
  "video_target_num_patches": 1024
}